Google Tracking How Busy Places are by Looking at Location History

Google’s Location History Patent

Google Maps helps people navigate from place to place.

In order for it to work effectively, it’s helpful if it can track the location of the device that someone may be using to help them navigate.

It’s interesting how Google tracks your location history. I’ve noticed that after I take a photo near a business, Google will sometimes ask if I would like to upload that photo to the business listing for that business. Sometimes the photos aren’t relevant to the business I’ve taken them near, such as a photo of an Agave Plant that I took near a Seaside Market in Cardiff-by-the-Sea, California.

Google seems to like the idea of saving location history for people who might search for different types of businesses, and a recent patent that I wrote about described how Google might start using distances from a location history as a ranking signal (as opposed to a static distance from a desktop computer.) I wrote about that in Google to Use Distance from Mobile Location History for Ranking in Local Search.

If you think about Google tracking individuals’ location histories in a different way, how else can that tracking history be useful to people? You may have noticed that Google now sometimes shows how busy a place might be different points in the day. That is from tracked location history aggregated. I saw someone ask about this on Twitter today, and it set me trying to find a patent from Google that described the details of how Google might be tracking how busy different businesses might be. I found one.

The patent I found tells us that it is about:

The present disclosure relates generally to determining a latency period at a user destination, and more particularly to methods and systems that rely on user-location histories, such as fine-grained user location data, to determine the latency period at a destination of a user. The present disclosure also relates to using latency period data in a variety of applications, including generation of a shopping route for a user.

Google is tracking how busy different businesses are based upon those user locations, and location history.

It tells us that being able to provide someone with planning details about a shopping trip can be useful, such as how long the trip to a business might be, as well as how long they might spend there. If someone asks for a chain business, knowing how busy the location is can also be helpful to a user, and the process described in this patent attempts to answer that problem as well. I hadn’t thought of how helpful it could be in the context of chain businesses until I read the patent:

While knowing the travel time and distance to a location is often helpful to a user, the user is left without knowing how busy the nearest location is or whether other, nearby locations are less busy. For example, the user does not know whether visiting a chain location that is slightly further away—but less busy or less crowded—may take less time overall than visiting the chain location that is nearby. Thus, based on travel time to the destination alone, the user may spend more time traveling to and visiting the nearest location than the user would if traveling to and visiting a location that is further away. And in some instances, a user may not care how long it takes to get to a point-of-interest. Rather, the user may desire only to know how long the wait is at a particular point-of-interest or how long it will take the user to pass through the point-of-interest, such as through a checkout line at a retailer. In addition to knowing how long a trip will take, in certain instances a user may wish to know the fastest route or alternate routes. For example, a user with a specific shopping list may desire the best route (or alternate routes) for obtaining the products on the shopping list.

Other information that might be provided include things like wait times at restaurants and how long it is taking people to check out at grocery stores,

Interestingly, fine-grained location history tracked could include the user device in a checkout line at a grocery store, or at the entrance area of a restaurant, or in a line at an amusement park. So, times spent waiting to buy groceries or waiting to be served a meal or time spent waiting for a ride could be reported to others who might consider going to that grocery store, or restaurant or amusement park. Mobile location information history looks like it could be useful.

I’m reminded of Google doing something similar with mobile devices and real-time traffic information, which I wrote about in 2006 in the post Ending Gridlock with Google Driving Assistance (Zipdash Re-Emerges). I guess if it worked with traffic time estimates, it might be worth using in other contexts, like grocery store lines or amusement park ride lines.

The patent is referring to this understanding of how busy a business might be as a “latency analysis system”, and tells us that it is based upon receiving location histories for multiple computing devices. The location history can tell how long each person was at a business in addition to telling how busy a business is at different times of a day.

The patent also points out that this latency information can be “real time” in providing current wait periods for restaurants, and so on.

This system can also tell users whether or not a location they might be planning on traveling to is open or closed, or possibly closing soon (or maybe hasn’t opened yet.)

The location history patent also describes another feature involving having a shopping list for products on your phone and being able to identify merchants who offer those products and generating a shopping route based upon those products and merchants offering them, and how long it would take to buy each item on the list.

If it is compiling a shopping route from your shopping list with locations to buy from, it may attempt to calculate the most efficient route.

In addition to telling us how busy a place may be, Google may also tell us how long we might take when we go some place, like averaging 20 minutes inside of this place:

There are aspects of this system that may use different data sources to reinforce data being collected. For instance, if location history information is being used to track time waiting to check out in a grocery store line, that timing information could possibly be checked up on by looking at electronic wallet information associated with purchased involved in a checkout at the grocery store.

The description of the location history patent provides more details and more examples and is worth spending time with.

A latency analysis system determines a latency period, such as a wait time, at a user destination. To determine the latency period, the latency analysis system receives location history from multiple user devices. With the location histories, the latency analysis system identifies points-of-interest that users have visited and determines the amount of time the user devices were at a point-of-interest. For example, the latency analysis system determines when a user device entered and exited a point-of-interest. Based on the elapsed time between entry and exit, the latency analysis system determines how long the user device was inside the point-of-interest. By averaging elapsed times for multiple user devices, the latency analysis system determines a latency period for the point-of-interest. The latency analysis system then uses the latency period to provide latency-based recommendations to a user. For example, the latency analysis system may determine a shopping route for a user.

Take-Aways

People carrying their phones around with them are providing useful information to others. We have in effect become Googlebot crawling the world with our navigation devices turned on. The patent tells us that Google is being careful by trying to avoid sharing and spreading personally identifiable information.

I am happy that Google asks for permission before it uses a photo that I’ve taken near a business before it assumes that the photo is of the business. When you opt-in to using location-based services on your phone, you are helping people decide which restaurants to choose to eat at, or grocery store to shop at or amusement park to visit. You are helping track location history or how long people tend to be at a business.

The patent does stress that they will take efforts to protect personally identifiable information.

I’m not sure that the process behind this does have financial and stock market implications, but I would suspect that if it does the legal team at Google would have considered those things, or may be looking into them. Aspects of the processes behind this patent have been put into place, so we aren’t talking hypotheticals, like we often see when I write about many patents. It’s something that we ideally should be watching out for.

Similarly I found I was supermarket shopping on a weekday and time that roughly coincided with the busiest day and time for every supermarket in my area. I changed the hours when I shopped. It saved a lot of time.

Undoubtedly informative and deeply helpful.
As to the screen shot:
Powerful. The place appeared to be roughly twice as busy as historical numbers would suggest. Very telling.
BTW: There was also an opportunity to respond and asked if the info was appropriate or not.

On the other hand accumulating the data they have with initially historical data, then their”representations of what historical data looks like:

Google has aggregate data on “traffic counts” or at least a powerful model from which to assess if places are busier or less busy. They have historical data that they can track against audited and reported financial results.

Its akin to “insider” information that is closely monitored by the SEC. Tie their data to audited financial data and they have tools for investment before the rest of the world, and of the nature of “insider” data.
Quite stunning. There are other potential issues. Simply there are financial institutions that would use this to make uber millions.

This aggregated data is only being shown in search results on a place-by-place basis, rather than collected and shown industry wide. That type of aggregation could potentially impact businesses. Google acquired a satellite analytics company in 2014 which you might find interesting, named Skybox Imaging. I looked up their patents, and this one was interesting: Using human intelligence tasks for precise image analysis. It is more clearly aimed at gathering of data for business intelligence uses.

Bill this is interesting for so many reasons. Imagine the future of local votes or backlinks is based on not how much time somebody spends on your site and visits but is based on how many people on average attend your restaurant and how long they stay compared to other restaurants in the same distinct category tied in with other ranking factors online.

Looking forward to how the machine learning process grows with this over the next few years.

Thanks. An interesting thought, that actual time at a business might end up becoming a ranking signal on the Web for that business. I would hope that doesn’t mean that places might purposefully slow down to delay people in a checkout line, or an amusement part ride line. I would guess that some wait times aren’t improved by being longer. It’s possible that data like this might be used with Artificial Intelligence.

Bill: I’ll have to go through the patent you referenced.
I’m aware how google is showing this information. What I’m referencing is the data google has and how folks inside Google could use it. In my mind that is what makes it akin to “insider trading” kinds of data.

That is why I have comments on this site, so that people can voice their opinions on blogs I write about. The patent does mention privacy, but tells us that information will be modified to protect the personally identifiable information of people whose locations are being tracked. No individuals are being identified, but yes a lot of information is being collected. Much like the information being tracked to enable Google to provide traffic time estimates, which I linked to in the post. I will point at this post in Twitter telling people to look at the comments for a discussion about privacy, inviting them to participate.

I think you’re going to have to explain to me how you see the process in this patent being similar to “insider trading” My notion of insider trading is that someone from inside of a company provides information about that company to other people who might use that information to gain an unfair advantage in the purchase of ownership (as in stocks) of the company. Google does seem to be providing business intelligence information, but I’m not seeing an intent to provide someone with an unfair advantage in the purchase of ownership of those companies. Thanks.

That said, I see the “privacy” issue to be a “deterrent” and not a stoppage for our gathered role as a type of “Google Bot” or other even more meaningful monitoring system.

Oh yes, I do see the world much more accountable today and much, much more tomorrow. All one need do is consider what is already readily available and linked to the monitoring of our PC’s and smartphones by parents, legal officials and John Q public.

I mean, do you know how many arrests that have already been made in 2017 which can be directly linked to the use of “Social Websites” like Facebook, Twitter, Pinterest and PBS Kids?

Much of the monitoring technology easily found today, involves “mobile devices” and I don’t need to share the connection. Do I?

Very detailed resource. I agree with previous comment that i also have doubt on accurate results because sometimes it not provide desired results otherwise if this problem sort out in future then it would be very beneficial for everyone specially for large businesses.

SEO by the Sea focuses upon SEO as the search engines tell us about it, from sources such as patents and white papers from the search engines. This information about SEO is tempered by years of experience from the author of the site, who has been doing SEO since the days when search engines started appearing on the Web.